Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography.

Many COVID-19 patients infected by SARS-CoV-2 virus develop pneumonia (called novel coronavirus pneumonia, NCP) and rapidly progress to respiratory failure. However, rapid diagnosis and identification of high-risk patients for early intervention are challenging. Using a large computed tomography (CT) database from 3,777 patients, we developed an AI system that can diagnose NCP and differentiate it from other common pneumonia and normal controls. The AI system can assist radiologists and physicians in performing a quick diagnosis especially when the health system is overloaded. Significantly, our AI system identified important clinical markers that correlated with the NCP lesion properties. Together with the clinical data, our AI system was able to provide accurate clinical prognosis that can aid clinicians to consider appropriate early clinical management and allocate resources appropriately. We have made this AI system available globally to assist the clinicians to combat COVID-19.

Evaluation of deep learning-based reconstruction late gadolinium enhancement images for identifying patients with clinically unrecognized myocardial infarction.

BACKGROUND: The presence of infarction in patients with unrecognized myocardial infarction (UMI) is a critical feature in predicting adverse cardiac events. This study aimed to compare the detection rate of UMI using conventional and deep learning reconstruction (DLR)-based late gadolinium enhancement (LGEO and LGEDL, respectively) and evaluate optimal quantification parameters to enhance diagnosis and management of suspected patients with UMI. METHODS: This prospective study included 98 patients (68 men; mean age: 55.8 ± 8.1 years) with suspected UMI treated at our hospital from April 2022 to August 2023. LGEO and LGEDL images were obtained using conventional and commercially available inline DLR algorithms. The myocardial signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and percentage of enhanced area (Parea) employing the signal threshold versus reference mean (STRM) approach, which correlates the signal intensity (SI) within areas of interest with the average SI of normal regions, were analyzed. Analysis was performed using the standard deviation (SD) threshold approach (2SD-5SD) and full width at half maximum (FWHM) method. The diagnostic efficacies based on LGEDL and LGEO images were calculated. RESULTS: The SNRDL and CNRDL were two times better than the SNRO and CNRO, respectively (P < 0.05). Parea-DL was elevated compared to Parea-O using the threshold methods (P < 0.05); however, no intergroup difference was found based on the FWHM method (P > 0.05). The Parea-DL and Parea-O also differed except between the 2SD and 3SD and the 4SD/5SD and FWHM methods (P < 0.05). The receiver operating characteristic curve analysis revealed that each SD method exhibited good diagnostic efficacy for detecting UMI, with the Parea-DL having the best diagnostic efficacy based on the 5SD method (P < 0.05). Overall, the LGEDL images had better image quality. Strong diagnostic efficacy for UMI identification was achieved when the STRM was ≥ 4SD and ≥ 3SD for the LGEDL and LGEO, respectively. CONCLUSIONS: STRM selection for LGEDL magnetic resonance images helps improve clinical decision-making in patients with UMI. This study underscored the importance of STRM selection for analyzing LGEDL images to enhance diagnostic accuracy and clinical decision-making for patients with UMI, further providing better cardiovascular care.

Neurodegeneration within the rostral spinal cord is associated with brain gray matter volume atrophy in the early stage of cervical spondylotic myelopathy.

STUDY DESIGN: Case-control study. OBJECTIVES: Investigating the association between neurodegeneration within rostral spinal cord and brain gray matter volume (GMV) and assessing the relationship between remote neurodegenerative changes and clinical outcomes at the early phase of Cervical Spondylotic Myelopathy (CSM). SETTING: University/hospital. METHODS: Using Spinal Cord Toolbox, spinal cord morphometrics (cross-sectional area [CSA], gray matter area [GMA], white matter area [WMA]) of 40 patients with CSM and 28 healthy controls (HCs) were computed and compared using two-sample t test. Brain GMV of the two groups was analyzed using voxel-based morphometry approach. Pearson's correlation between spinal cord morphometrics and altered brain GMV and Spearman's relationship between remote neurodegenerations and clinical outcomes were conducted in CSM group. RESULTS: Compared to HCs, CSA and WMA at C2/3 and GMV in right postcentral gyrus (PoCG.R) and left supplementary motor area (SMA.L) were significantly decreased in patients with CSM. CSA and WMA at C2/3 were associated with GMV in SMA.L and MCG.R in patients with CSM. CSA at C2/3 and GMV in PoCG.R were related to modified Japanese Orthopedic Association score in patients with CSM. CONCLUSIONS: The associations between CSA and WMA at C2/3 and GMV in SMA.L and MCG.R suggest a concordant change pattern and adaptive mechanisms for neuronal plasticity underlying remote neurodegeneration in early CSM. The atrophy of CSA at C2/3 and GMV loss in PoCG.R can serve as potential neuroimaging biomarkers of early structural changes within spinal cord and brain preceding marked clinical disabilities in patients with CSM.

Radiomics analysis enables fatal outcome prediction for hospitalized patients with coronavirus disease 2019 (COVID-19).

BACKGROUND: In December 2019, a rare respiratory disease named coronavirus disease 2019 (COVID-19) broke out, leading to great concern around the world. PURPOSE: To develop and validate a radiomics nomogram for predicting the fatal outcome of COVID-19 pneumonia. MATERIAL AND METHODS: The present study consisted of a training dataset (n = 66) and a validation dataset (n = 30) with COVID-19 from January 2020 to March 2020. A radiomics signature was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics score (Rad-score) was developed from the training cohort. The radiomics model, clinical model, and integrated model were built to assess the association between radiomics signature/clinical characteristics and the mortality of COVID-19 cases. The radiomics signature combined with the Rad-score and the independent clinical factors and radiomics nomogram were constructed. RESULTS: Seven stable radiomics features associated with the mortality of COVID-19 were finally selected. A radiomics nomogram was based on a combined model consisting of the radiomics signature and the clinical risk factors indicating optimal predictive performance for the fatal outcome of patients with COVID-19 with a C-index of 0.912 (95% confidence interval [CI] 0.867-0.957) in the training dataset and 0.907 (95% CI 0.849-0.966) in the validation dataset. The calibration curves indicated optimal consistency between the prediction and the observation in both training and validation cohorts. CONCLUSION: The CT-based radiomics nomogram indicated favorable predictive efficacy for the overall survival risk of patients with COVID-19, which could help clinicians intensively follow up high-risk patients and make timely diagnoses.

3D UTE bicomponent imaging of cortical bone using a soft-hard composite pulse for excitation.

PURPOSE: To evaluate 3D UTE bicomponent imaging of cortical bone ex vivo and in vivo using a newly designed soft-hard composite pulse for excitation. METHODS: Chemical shift artifacts, presenting as fat-water oscillation or combination-induced signal oscillation, significantly reduce the accuracy of quantitative UTE bicomponent analysis of cortical bone. To achieve fat suppression for more reliable bicomponent analysis, a newly developed soft-hard excitation pulse was used with UTE imaging and compared with a single rectangular pulse excitation without and with a conventional fat saturation (FatSat) module. These 3 sequences were applied to 8 bovine bone samples without marrow fat, 3 bovine bone samples with marrow fat, and tibial midshafts of 5 healthy human volunteers. Bicomponent analyses were performed in both ex vivo and in vivo studies. RESULTS: The soft-hard pulse provided comparable fat suppression, but much reduced bone signal attenuation compared with the FatSat module. Better bicomponent T2∗ fitting was also achieved with the soft-hard excitation pulse because it greatly reduced chemical shift artifacts and outperformed the single rectangular pulse without or with FatSat. Although the FatSat module reduced fat signals and related fat-water oscillation, the water signals were significantly attenuated with more than 40% reduction due to direction saturation. For the inner layer of tibial midshaft in healthy volunteers, fitting errors increased from 3.78% for the soft-hard pulse to 11.43% and 5.16%, respectively, for the single rectangular pulse without and with the FatSat module. CONCLUSION: The 3D UTE sequence with a new soft-hard excitation pulse allows more reliable bicomponent imaging of cortical bone.

Development and multicenter validation of a CT-based radiomics signature for predicting severe COVID-19 pneumonia.

OBJECTIVES: To develop and validate a radiomics nomogram for timely predicting severe COVID-19 pneumonia. MATERIALS AND METHODS: Three hundred and sixteen COVID-19 patients (246 non-severe and 70 severe) were retrospectively collected from two institutions and allocated to training, validation, and testing cohorts. Radiomics features were extracted from chest CT images. Radiomics signature was constructed based on reproducible features using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm with 5-fold cross-validation. Logistic regression modeling was employed to build different models based on quantitative CT features, radiomics signature, clinical factors, and/or the former combined features. Nomogram performance for severe COVID-19 prediction was assessed with respect to calibration, discrimination, and clinical usefulness. RESULTS: Sixteen selected features were used to build the radiomics signature. The CT-based radiomics model showed good calibration and discrimination in the training cohort (AUC, 0.9; 95% CI, 0.843-0.942), the validation cohort (AUC, 0.878; 95% CI, 0.796-0.958), and the testing cohort (AUC, 0.842; 95% CI, 0.761-0.922). The CT-based radiomics model showed better discrimination capability (all p < 0.05) compared with the clinical factors joint quantitative CT model (AUC, 0.781; 95% CI, 0.708-0.843) in the training cohort, the validation cohort (AUC, 0.814; 95% CI, 0.703-0.897), and the testing cohort (AUC, 0.696; 95% CI, 0.581-0.796). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics model outperformed the clinical factors model and quantitative CT model alone. CONCLUSIONS: The CT-based radiomics signature shows favorable predictive efficacy for severe COVID-19, which might assist clinicians in tailoring precise therapy. KEY POINTS: • Radiomics can be applied in CT images of COVID-19 and radiomics signature was an independent predictor of severe COVID-19. • CT-based radiomics model can predict severe COVID-19 with satisfactory accuracy compared with subjective CT findings and clinical factors. • Radiomics nomogram integrated with the radiomics signature, subjective CT findings, and clinical factors can achieve better severity prediction with improved diagnostic performance.

Evaluation of microvascular permeability of skeletal muscle and texture analysis based on DCE-MRI in alloxan-induced diabetic rabbits.

OBJECTIVES: To estimate the microvascular permeability and perfusion of skeletal muscle by using quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and explore the feasibility of using texture analysis (TA) to evaluate subtle structural changes of diabetic muscles. METHODS: Twenty-four rabbits were randomly divided into diabetic (n = 14) and control (n = 10) groups, and underwent axial DCE-MRI of the multifidus muscle (0, 4, 8, 12, and 16 weeks after alloxan injection). The pharmacokinetic model was used to calculate the permeability parameters; texture parameters were extracted from volume transfer constant (Ktrans) map. The two-sample t test/Mann-Whitney U test, repeated measures analysis of variance/Friedman test, and Pearson correlations were used for data analysis. RESULTS: In the diabetic group, Ktrans and rate constant (Kep) increased significantly at week 8 and then showed a decreasing trend. Extravascular extracellular space volume fraction (Ve) increased and plasma volume fraction (Vp) decreased significantly from the 8th week. Skewness began to decrease at the 4th week. Median Ktrans and entropy increased significantly, while inverse difference moment decreased from the 8th week. Energy decreased while contrast increased only at week 8. Muscle fibre cross-sectional area was negatively correlated with Ve. The capillary-to-fibre ratio was positively correlated with Vp (p < 0.05, all). CONCLUSIONS: Quantitative DCE-MRI can be used to evaluate microvascular permeability and perfusion in diabetic skeletal muscle at an early stage; TA based on Ktrans map can identify microarchitectural modifications in diabetic muscles. KEY POINTS: • Four quantitative parameters of DCE-MRI can be used to evaluate microvascular permeability and perfusion of skeletal muscle in diabetic models at early stages. • Texture analysis based on Ktrans map can identify subtle structural changes in diabetic muscles.

A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis.

Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19 and finding high-risk patients with worse prognosis for early prevention and medical resource optimisation is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography.We retrospectively collected 5372 patients with computed tomography images from seven cities or provinces. Firstly, 4106 patients with computed tomography images were used to pre-train the deep learning system, making it learn lung features. Following this, 1266 patients (924 with COVID-19 (471 had follow-up for >5 days) and 342 with other pneumonia) from six cities or provinces were enrolled to train and externally validate the performance of the deep learning system.In the four external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC 0.87 and 0.88, respectively) and viral pneumonia (AUC 0.86). Moreover, the deep learning system succeeded to stratify patients into high- and low-risk groups whose hospital-stay time had significant difference (p=0.013 and p=0.014, respectively). Without human assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.Deep learning provides a convenient tool for fast screening of COVID-19 and identifying potential high-risk patients, which may be helpful for medical resource optimisation and early prevention before patients show severe symptoms.

A comparative study of diffusion kurtosis imaging and T2* mapping in quantitative detection of lumbar intervertebral disk degeneration.

PURPOSE: To assess the feasibility of diffusion kurtosis imaging (DKI) for diagnosing lumbar intervertebral disk degeneration (IDD) and to compare the potential of DKI and T2* mapping in the diagnosis of early IDD. METHODS: Sagittal T2WI, DKI, and T2* mapping were performed in 75 subjects with 375 lumbar intervertebral disks at a 3.0-T MRI. DKI-related parameters including mean kurtosis (MK), mean diffusivity (MD), fractional anisotropy (FA), and T2* values were calculated for each disk which was segmented into three regions: nucleus pulposus (NP), anterior annulus fibrosus (AAF), and posterior annulus fibrosus (PAF). RESULTS: MK and FA were positively correlated with Pfirrmann grade (all P < 0.001). MD and T2* were negatively correlated with Pfirrmann grade (all P < 0.001) except for T2* value of AAF (r = 0.087, P > 0.05). MK and FA values increased, while MD and T2* values decreased with age. No statistical significance was found between men and women (P > 0.05). Cephalic lumbar disks (L1/L2 and L2/L3) got lower MK and FA values than caudal lumbar disks (L4/L5 and L5/S1) (all P < 0.05), while cephalic lumbar disks got higher MD value than caudal lumbar disks (all P < 0.05). ROC analysis demonstrated that MK, MD, and FA showed significantly higher diagnostic accuracies than T2*, especially in NP and PAF. CONCLUSIONS: DKI can be used to assess human lumbar IDD. And DKI was more sensitive to the quantitative detection of early lumbar IDD than T2* mapping. These slides can be retrieved under Electronic Supplementary Material.

Posterior reversible encephalopathy syndrome complicated with subarachnoid hemorrhage in an eclamptic pregnant patient: case report.

BACKGROUND: Posterior reversible encephalopathy syndrome (PRES) is a neurotoxic condition which comprises various neurological symptoms. This syndrome could be complicated by intracranial hemorrhage including subarachnoid hemorrhage (SAH). However, SAH is rarely seen in eclamptic patients with PRES. CASE PRESENTATION: A 34-weeks-pregnant woman at the age of 33 was admitted to the obstetrics department because of an episode of generalized tonic-clonic seizure. Before the seizure, the patient had a headache and was found to have an abnormal systolic blood pressure of 160 mmHg. On admission, systolic and diastolic blood pressures were up to 182 and 99 mmHg, respectively. Emergent cesarean section was then performed. On hospital day (HD) 2, cranial non-contrast computed tomography (CT) revealed the existence of SAH. Multiple areas of high signals on T2-weighted and fluid attenuated inversion recovery (FLAIR) sequences were shown by cranial magnetic resonance imaging (MRI) performed 2 days later. CT-angiography studies didn't reveal intracranial aneurysm. After anti-hypertensive treatment, arterial blood pressure of the patient was gradually tapered to normal values. Eventually, the patient was discharged without any residual symptoms. CONCLUSIONS: SAH is a rare complication of PRES in eclamptic patients. In patients with PRES, occurrence of SAH is related to increased morbidity and mortality especially when the hemorrhage is diffuse or massive. Our patient had a minor hemorrhage. The good prognosis might also be due to immediate elimination of the risk factor of PRES by emergent delivery.

Nonrigid registration with corresponding points constraint for automatic segmentation of cardiac DSCT images.

BACKGROUND: Dual-source computed tomography (DSCT) is a very effective way for diagnosis and treatment of heart disease. The quantitative information of spatiotemporal DSCT images can be important for the evaluation of cardiac function. To avoid the shortcoming of manual delineation, it is imperative to develop an automatic segmentation technique for 4D cardiac images. METHODS: In this paper, we implement the heart segmentation-propagation framework based on nonrigid registration. The corresponding points of anatomical substructures are extracted by using the extension of n-dimensional scale invariant feature transform method. They are considered as a constraint term of nonrigid registration using the free-form deformation, in order to restrain the large variations and boundary ambiguity between subjects. RESULTS: We validate our method on 15 patients at ten time phases. Atlases are constructed by the training dataset from ten patients. On the remaining data the median overlap is shown to improve significantly compared to original mutual information, in particular from 0.4703 to 0.5015 ([Formula: see text]) for left ventricle myocardium and from 0.6307 to 0.6519 ([Formula: see text]) for right atrium. CONCLUSIONS: The proposed method outperforms standard mutual information of intensity only. The segmentation errors had been significantly reduced at the left ventricle myocardium and the right atrium. The mean surface distance of using our framework is around 1.73 mm for the whole heart.

Feasibility of ASL spinal bone marrow perfusion imaging with optimized inversion time.

PURPOSE: To assess the correlation between flow-sensitive alternating inversion recovery (FAIR) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in the measurement of spinal bone marrow (SBM) perfusion; in addition, to assess for an optimized inversion time (TI) as well as the reproducibility of SBM FAIR perfusion. MATERIALS AND METHODS: The optimized TI of a FAIR SBM perfusion experiment was carried out on 14 volunteers; two adjacent vertebral bodies were selected from each volunteer to measure the change of signal intensity (ΔM) and the signal-to-noise ratio (SNR) of FAIR perfusion MRI with five different TIs. Then, reproducibility of FAIR data from 10 volunteers was assessed by the reposition SBM FAIR experiments. Finally, FAIR and DCE-MRI were performed on 27 subjects. The correlation between the blood flow on FAIR (BFASL ) and perfusion-related parameters on DCE-MRI was evaluated. RESULTS: The maximum value of ΔM and SNR were 36.39 ± 12.53 and 2.38 ± 0.97, respectively; both were obtained when TI was near 1200 msec. There were no significant difference between the two successive measurements of SBM BFASL perfusion (P = 0.879), and the within-subject coefficients of variation (wCV) of the measurements was 3.28%. The BFASL showed a close correlation with K(trans) (P < 0.001) and Kep (P = 0.004), and no correlation with Ve (P = 0.082) was found. CONCLUSION: 1200 msec was the optimal TI for the SBM ASL perfusion image, which led to the maximum ΔM and a good quality perfusion image. The SBM FAIR perfusion scan protocol has good reproducibility, and as blood flow measurement on FAIR is reliable and closely related with the parameters on DCE-MRI, FAIR is feasible for measuring SBM blood flow.

Matrix metalloproteinase 9 expression and glioblastoma survival prediction using machine learning on digital pathological images.

This study aimed to apply pathomics to predict Matrix metalloproteinase 9 (MMP9) expression in glioblastoma (GBM) and investigate the underlying molecular mechanisms associated with pathomics. Here, we included 127 GBM patients, 78 of whom were randomly allocated to the training and test cohorts for pathomics modeling. The prognostic significance of MMP9 was assessed using Kaplan-Meier and Cox regression analyses. PyRadiomics was used to extract the features of H&E-stained whole slide images. Feature selection was performed using the maximum relevance and minimum redundancy (mRMR) and recursive feature elimination (RFE) algorithms. Prediction models were created using support vector machines (SVM) and logistic regression (LR). The performance was assessed using ROC analysis, calibration curve assessment, and decision curve analysis. MMP9 expression was elevated in patients with GBM. This was an independent prognostic factor for GBM. Six features were selected for the pathomics model. The area under the curves (AUCs) of the training and test subsets were 0.828 and 0.808, respectively, for the SVM model and 0.778 and 0.754, respectively, for the LR model. The C-index and calibration plots exhibited effective estimation abilities. The pathomics score calculated using the SVM model was highly correlated with overall survival time. These findings indicate that MMP9 plays a crucial role in GBM development and prognosis. Our pathomics model demonstrated high efficacy for predicting MMP9 expression levels and prognosis of patients with GBM.

Microvascular permeability and texture analysis of bone marrow in diabetic rabbits with critical limb ischemia based on dynamic contrast-enhanced magnetic resonance imaging.

BACKGROUND: Early on in the development of diabetes, skeletal muscles can exhibit microarchitectural changes that can be detected using texture analysis (TA) based on volume transfer constant (Ktrans) maps. Nevertheless, there have been few studies and thus we evaluated microvascular permeability and the TA of the bone marrow in diabetics with critical limb ischemia (CLI). METHODS: Eighteen male rabbits were randomly assigned equally into an operation group with hindlimb ischemia and diabetes, a sham-operated group with diabetes only, and a control group. Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) was performed on all rabbits at predetermined intervals (1, 5, 10, 15, 20, and 25 days post-surgery). The pharmacokinetic model was used to generate the permeability parameters, while the textural parameters were derived from the Ktrans map. Data analysis methods included the independent sample t-test, Mann-Whitney U test, repeated-measures analysis of variance, and Pearson correlation tests. RESULTS: The Ktrans values reached a minimum on day 1 after ischemia induction, then gradually recovered, but remained lower than those of the sham-operated group. The volume fraction only showed a significant difference between the operation group and the sham-operated group on day 5 post-surgery, but not in the extravascular extracellular space volume fraction at all time points. A significantly reduced Ktrans on day 1, a decreased number of bone trabeculae (Tb.N), and the area of bone trabeculae (Tb.Ar), and an increased microvessel density on day 25 in the operation group compared with the sham-operated group were observed. At each time point, there was a discernible difference between the two groups in the mean value, mean of positive pixels, and sumAverage. CONCLUSIONS: The early stages of diabetic bone marrow with CLI can be evaluated by DCE-MRI for microvascular permeability. Texture analysis based on DCE-MRI could act as an imaging discriminator and new radiological analysis tool for critical limb ischemia in diabetes mellitus.

Deep learning-based radiomics of computed tomography angiography to predict adverse events after initial endovascular repair for acute uncomplicated Stanford type B aortic dissection.

PURPOSE: This study aimed to construct a predictive model integrating deep learning-derived radiomic features from computed tomography angiography (CTA) and clinical biomarkers to forecast postoperative adverse events (AEs) in patients with acute uncomplicated Stanford type B aortic dissection (uTBAD) undergoing initial thoracic endovascular aortic repair (TEVAR). METHODS: We retrospectively evaluated 369 patients treated with TEVAR for acute uTBAD from January 2015 to December 2022. A three-dimensional (3D) deep convolutional neural network (CNN) automated radiomic feature extraction from CTA images. Feature selection, using Analysis of Variance (ANOVA) and the Least Absolute Shrinkage and Selection Operator (LASSO) algorithms, refined a radiomic score (Rad-Score). This score, alongside clinical parameters, was modelled via Extreme Gradient Boosting (XGBoost) analysis. Model calibration was assessed by calibration curves. RESULTS: The integration of the Rad-Score with clinical factors including albumin and C-reactive protein levels moderately enhanced predictive efficiency, exhibiting an area under the curve (AUC) of 1.000 (95%CI, 1.000-1.000) in the training cohort and 0.990 (95%CI, 0.966-1.000) in the internal validation cohort. In an independent validation cohort from another hospital, the combined model yielded an AUC of 0.985 (95%CI, 0.965-1.000), with an accuracy, precision, sensitivity, and specificity of 0.92, 0.92, 0.94, and 0.91, respectively. CONCLUSIONS: The synergistic application of deep learning-based radiomics from CTA and clinical indicators holds promise for anticipating AEs post-initial thoracic endovascular aortic repair in patients with acute uTBAD. The clinical utility of the constructed combined model, offering prognostic foresight during follow-up, has been substantiated.

Atrial and Ventricular Strain Imaging Using CMR in the Prediction of Ventricular Arrhythmia in Patients with Myocarditis.

(1) Objective: Myocarditis can be associated with ventricular arrhythmia (VA), individual non-invasive risk stratification through cardiovascular magnetic resonance (CMR) is of great clinical significance. Our study aimed to explore whether left atrial (LA) and left ventricle (LV) myocardial strain serve as independent predictors of VA in patients with myocarditis. (2) Methods: This retrospective study evaluated CMR scans in 141 consecutive patients diagnosed with myocarditis based on the updated Lake Louise criteria (29 females, mean age 41 ± 20). The primary endpoint was VA; this encompassed ventricular fibrillation, sustained ventricular tachycardia, nonsustained ventricular tachycardia, and frequent premature ventricular complexes. LA and LV strain function were performed on conventional cine SSFP sequences. (3) Results: After a median follow-up time of 23 months (interquartile range (18-30)), 17 patients with acute myocarditis reached the primary endpoint. In the multivariable Cox regression analysis, LA reservoir (hazard ratio [HR] and 95% confidence interval [CI]: 0.93 [0.87-0.99], p = 0.02), LA booster (0.87 95% CI [0.76-0.99], p = 0.04), LV global longitudinal (1.26 95% CI [1.02-1.55], p = 0.03), circumferential (1.37 95% CI [1.08-1.73], p = 0.008), and radial strain (0.89 95% CI [0.80-0.98], p = 0.01) were all independent determinants of VA. Patients with LV global circumferential strain > -13.3% exhibited worse event-free survival compared to those with values ≤ -13.3% (p < 0.0001). (4) Conclusions: LA and LV strain mechanism on CMR are independently associated with VA events in patients with myocarditis, independent to LV ejection fraction, and late gadolinium enhancement location. Incorporating myocardial strain parameters into the management of myocarditis may improve risk stratification.

Development and Validation of a Deep Learning Model to Reduce the Interference of Rectal Artifacts in MRI-based Prostate Cancer Diagnosis.

Purpose To develop an MRI-based model for clinically significant prostate cancer (csPCa) diagnosis that can resist rectal artifact interference. Materials and Methods This retrospective study included 2203 male patients with prostate lesions who underwent biparametric MRI and biopsy between January 2019 and June 2023. Targeted adversarial training with proprietary adversarial samples (TPAS) strategy was proposed to enhance model resistance against rectal artifacts. The automated csPCa diagnostic models trained with and without TPAS were compared using multicenter validation datasets. The impact of rectal artifacts on the diagnostic performance of each model at the patient and lesion levels was compared using the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPRC). The AUC between models was compared using the DeLong test, and the AUPRC was compared using the bootstrap method. Results The TPAS model exhibited diagnostic performance improvements of 6% at the patient level (AUC: 0.87 vs 0.81, P < .001) and 7% at the lesion level (AUPRC: 0.84 vs 0.77, P = .007) compared with the control model. The TPAS model demonstrated less performance decline in the presence of rectal artifact-pattern adversarial noise than the control model (ΔAUC: -17% vs -19%, ΔAUPRC: -18% vs -21%). The TPAS model performed better than the control model in patients with moderate (AUC: 0.79 vs 0.73, AUPRC: 0.68 vs 0.61) and severe (AUC: 0.75 vs 0.57, AUPRC: 0.69 vs 0.59) artifacts. Conclusion This study demonstrates that the TPAS model can reduce rectal artifact interference in MRI-based csPCa diagnosis, thereby improving its performance in clinical applications. Keywords: MR-Diffusion-weighted Imaging, Urinary, Prostate, Comparative Studies, Diagnosis, Transfer Learning Clinical trial registration no. ChiCTR23000069832 Supplemental material is available for this article. Published under a CC BY 4.0 license.

[Clinical research of early intervention of modified shuyu pill in vascular cognitive impairment no dementia].

OBJECTIVE: To observe early intervention effects of Modified Shuyu Pill (MSP) on vascular cognitive impairment no dementia (VCIND). METHODS: Totally 100 patients VCIND were randomly assigned to the treatment group (43 cases) and the control group (33 cases). On the basis of the treatment targeting risk factors of blood vessels, patients in the treatment group were treated by MSP, while those in the control group were treated by donepezil hydrochloride. The therapeutic course was 16 weeks. The neuropsychological scales [mini-mental state examination (MMSE) and Montreal cognitive assessment (MOCA) score] and Chinese medicine dementia syndromes scales were observed before and after treatment. RESULTS: The MMSE and MOCA score of the two groups increased when compared with the same group before treatment (P < 0.01). But there was no statistical difference in MMSE or MOCA score after treatment between the two groups (P > 0.05). The Chinese medicine dementia syndromes scales significantly decreased in the treatment group when compared with before treatment (P < 0.01). But there was no statistical difference in Chinese medicine dementia syndromes scales in the control group between before and after treatment (P > 0.05). There was statistical difference in Chinese medicine dementia syndromes scales after treatment between the two groups (P < 0.01). CONCLUSION: MSP could effectively intervene the progress of VCIND.

[The gene mutation screening of a family with congenital fibrosis of the extraocular muscles associated with corpus callosum agenesis].

OBJECTIVE: To identify TUBB3 gene mutations in a Chinese family with congenital fibrosis of the extraocular muscle associated with corpus callosum agenesis. METHODS: We have found a family with CFEOM associated with corpus callosum agenesis, including 4 affected individuals in three generations of 11 familial members. 4 affected individuals were sequenced by direct TUBB3 sequencing, 4 unaffected individuals in the family and 100 cases of unrelated normal person as a control. RESULTS: This family is in line with Mendelian autosomal dominant inheritance. Clinical manifestations belongs to CFEOM3. All affected individuals were detected with TUBB3 c.1249G > A mutation, the mutation is in exon 4, resulting in wild-type gene encoding the Aspartic acid ( Asp or D ) replaced .by Asparagine (Asn or N ). CONCLUSION: Our study supports that TUBB3 gene mutation c.1249G > A (p. Asp417Asn), is the underlying molecular pathogenesis of this family with CFEOM3.